肌肉浸润性膀胱癌的分类，以预测接受免疫治疗的患者的预后。
Classification of Muscle Invasive Bladder Cancer to Predict Prognosis of Patients Treated with Immunotherapy.
机构信息
Department of Urology, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Henan University People's Hospital, Zhengzhou 450003, China.
Department of Oncology, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Henan University People's Hospital, Zhengzhou 450003, China.
出版信息
J Immunol Res. 2022 May 30;2022:6737241. doi: 10.1155/2022/6737241. eCollection 2022.
BACKGROUND
Recently, immunotherapies have been approved for advanced muscle invasive bladder cancer (MIBC) treatment, but only a small fraction of MIBC patients could achieve a durable drug response. Our study is aimed at identifying tumor microenvironment (TME) subtypes that have different immunotherapy response rates.
METHODS
The mRNA expression profiles of MIBC samples from seven discovery datasets (GSE13507, GSE31684, GSE32548, GSE32894, GSE48075, GSE48276, and GSE69795) were analyzed to identify TME subtypes. The identified TME subtypes were then validated by an independent dataset (TCGA-MIBC). The subtype-related biomarkers were discovered using computational analyses and then utilized to establish a random forest predictive model. The associations of TME subtypes with immunotherapy therapeutic responses were investigated in a group of patients who had been treated with immunotherapy. A prognostic index model was constructed using the subtype-related biomarkers. Two nomograms were built by the subtype-related biomarkers or the clinical parameters.
RESULTS
Two TME subtypes, including ECM-enriched class (EC) and immune-enriched class (IC), were found. EC was associated with greater extracellular matrix (ECM) pathways, and IC was correlated with immune pathways, respectively. Overall survival was significantly greater for tumors classified as IC, whereas the EC subtype had a worse prognosis. A total of nine genes (AKAP12, APOL3, CXCL13, CXCL9, GBP4, LRIG1, PEG3, PODN, and PTPRD) were selected by computational analyses to construct the random forest model. The area under the curve (AUC) values for this model were 0.827 and 0.767 in the testing and external validation datasets, respectively. Therapeutic response rates were greater in IC patients than in EC patients (28 percent vs. 18 percent). Patients with a high prognostic index had a poorer prognosis than those with a low prognostic index. The nomogram constructed from nine genes and stage achieved a C-index of 0.71.
CONCLUSION
The present investigation defined two distinct TME subtypes and developed models to assess immunotherapeutic treatment outcomes.
背景
最近，免疫疗法已被批准用于治疗晚期肌层浸润性膀胱癌（MIBC），但只有一小部分 MIBC 患者能够获得持久的药物反应。我们的研究旨在确定具有不同免疫治疗反应率的肿瘤微环境（TME）亚型。
方法
对来自七个发现数据集（GSE13507、GSE31684、GSE32548、GSE32894、GSE48075、GSE48276 和 GSE69795）的 MIBC 样本的 mRNA 表达谱进行分析，以识别 TME 亚型。通过独立数据集（TCGA-MIBC）对鉴定的 TME 亚型进行验证。使用计算分析发现与亚型相关的生物标志物，然后利用这些生物标志物建立随机森林预测模型。在一组接受免疫治疗的患者中研究 TME 亚型与免疫治疗治疗反应的相关性。使用与亚型相关的生物标志物构建预后指数模型。使用与亚型相关的生物标志物或临床参数构建两个列线图。
结果
发现了两种 TME 亚型，包括富含细胞外基质（EC）的类（EC）和富含免疫的类（IC）。EC 与更多的细胞外基质（ECM）途径相关，IC 与免疫途径相关。分类为 IC 的肿瘤的总体生存率明显更高，而 EC 亚型的预后较差。通过计算分析共选择了 9 个基因（AKAP12、APOL3、CXCL13、CXCL9、GBP4、LRIG1、PEG3、PODN 和 PTPRD）来构建随机森林模型。该模型在测试和外部验证数据集中的 AUC 值分别为 0.827 和 0.767。IC 患者的治疗反应率高于 EC 患者（28%比 18%）。高预后指数的患者预后较差，低预后指数的患者预后较好。由九个基因和阶段构建的列线图的 C 指数为 0.71。
结论
本研究定义了两种不同的 TME 亚型，并建立了评估免疫治疗治疗效果的模型。
Zotero 插件